AI share of voice is a brand’s percentage of total brand mentions across AI-generated answers to a defined prompt set, measured per engine or aggregated. The term circulates across the industry with inconsistent definitions; the version here is the one blimpp measures against, stated precisely so results can be compared over time and across engines.
In one sentence
AI share of voice carries the familiar share-of-voice idea into AI answers: of all the times any brand gets named, how many of those namings are yours.
How AI share of voice is calculated
AI share of voice = (mentions of the brand ÷ total brand mentions in the answer set) × 100
Every naming of any brand across all responses counts once in the denominator, including repeat namings within a single answer if measured at mention level, or once per answer if measured at answer level; the chosen basis must be stated with the result. A mention counts regardless of sentiment or whether it comes with a recommendation. Definitions of mention versus recommendation versus citation follow the measurement methodology; sampling follows the repeated-run protocol.
Worked example
A 25-prompt category set is run four times each on one engine, producing 100 responses containing 400 brand namings in total. The target brand is named 52 times, an AI share of voice of 13%. Illustrative example: blimpp benchmark data is added to this page as studies publish.
Why AI share of voice matters
Share of voice is the bridge metric for marketing teams arriving from traditional measurement: it needs no new mental model, only a new surface. It sets the competitive frame within which the sharper metrics operate. A brand with 40% share of voice but low recommendation share has a sentiment or framing problem; a brand with 5% share of voice has a retrieval problem. The metric’s weakness is that it counts hostile and incidental namings equally, which is why it is reported alongside recommendation share rather than instead of it.
What affects AI share of voice
Evidenced factors: how widely the brand appears across the sources engines retrieve for the category, since answers are composed from retrieved material. Factors with practitioner evidence: category prominence in comparison and listicle content, presence in community discussion for the prompt class, and brand distinctiveness, since ambiguous names are harder for systems to attribute confidently. These are treated as working hypotheses until tested.
Related concepts
References
- Martinez, O. (2026), Optimizing Visibility in Generative Engines: A Critical Survey of GEO: arxiv.org
Author: Harpal Singh · Last reviewed: 7 August 2026